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Injury Prevention 1998;4:126–131
Impact of a theory based intervention to increase bicycle helmet use in low income children Sherry Garrett Hendrickson, Heather Becker
Abstract Objective—While community interventions to increase bicycle helmet use have increased markedly, few of these studies are theoretically based. The purpose of this study was to determine relationships among PRECEDE model predictors and self reported helmet use among 407 fourth graders from nine low income, non-urban schools. Setting—Low income schools, with high minority populations in eight nonmetropolitan Central Texas counties were chosen. Methods—Schools were randomly assigned in a repeated measures design to either classroom only, parent-child, or control groups. School nurses were educated by the researchers to present a head injury prevention program in all but the experimental schools. Researchers made contact by phone with the parents of children in the parent-child group. Results and conclusions—Participation in either of the educational interventions, followed by belief that helmets protect your head (a predisposing factor), and participation in the parent intervention condition, added significant unique variance to the prediction of helmet use after helmet ownership is accounted. These four variables, taken together, account for 72% of the variance in predicting bicycle helmet use. (Injury Prevention 1998;4:126–131) Keywords: PRECEDE model; head injury prevention; theory based intervention; bicycle helmets
University of Texas at Austin, School of Nursing, 1700 Red River, Austin, TX 78701, USA S Garrett Hendrickson H Becker Correspondence to: Sherry Garrett Hendrickson.
As research funding grows ever more scarce, and health care strains to support long term care for injured patients, programs to prevent traumatic brain injury are increasing. Children younger than 15 years old account for 71% of all cyclist morbidity, and 33% of all bicyclist related deaths, despite the fact that children represent only 40% of cyclists.1 Bicycle helmets are known to decrease risk of head injury by 74–85%.2 This fact, however, may be unknown to today’s parenting generation, who, as children, rode without helmets. Because bicycle helmet use is an eVective deterrent to head injury, numerous worldwide eVorts,3 many presented at the International Conference on Helmet Initiatives,4 have been undertaken to educate the public about the importance of bicycle helmet use and to make bicycle helmets more accessible. However, a
theoretical base underlying these interventions is notably lacking. A popular evaluation model used in health education and injury prevention, and highlighted at the International Conference on Helmet Initiatives, is PRECEDE. This model identifies predisposing, enabling, and reinforcing educational factors surrounding an intervention. Predisposing factors include knowledge, attitude, beliefs, values, and perceptions that provide the motivation for behavior. Enabling factors support a desired behavior change (for example, helmet ownership, as requisite for helmet use). Reinforcing factors provide reward, incentive, or punishment for a behavior to be perpetuated or terminated. These components of the model are considered antecedents to behavioral change and were implemented in the intervention and the assessment of the intervention described in the present study. Empirical evidence suggests that various interventions have increased helmet use,5–7 yet knowledge of the specific combination of factors most predictive of helmet use is limited,8 9 as is a clear theoretical basis. Hierarchical multiple regression was used in the present study to predict bicycle helmet use as an outcome of a school based program, guided by the PRECEDE model.10 The study addressed the following research question: How much do predisposing, enabling, reinforcing factors (as identified in the PRECEDE model), and participation in an educational intervention, add to the prediction of reported bicycle helmet use, after controlling for helmet ownership? Over 600 published applications of the PRECEDE model for health promotion planning had appeared by 1997.11 In one of only two helmet studies found using the PRECEDE framework,9 12 persuasive communication, community organization, the joint use of traditional educational and reinforcing activities, and the enabling factor of increasing helmet access, boosted use by 23%.9 The program targeted elementary age students, 5 to 12 years old, in one region in Quebec. Before and after program helmet use was observed at three sites in poor and average rich areas of the municipality. In comparison, our nine community study included a control group, and focused on the age group of 10–12 year olds who are at highest risk for bicycling related morbidity and mortality.13 Method A multiple regression model was employed to determine how much variance in children’s self
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Theory based intervention Table 1
Correlation between PRECEDE model variables, intervention conditions, demographics, and self reported helmet use
Predictor variables Intervention conditions Parent and classroom Control group Classroom only Demographics Ethnicity: 1=whites (188); 2=other (142) Gender: 1=boy; 2=girl Helmet ownership (1=yes owns; 2=no) Predisposing factors A helmet doesn’t protect your head Very bad head injuries can change you forever I can control my bike so well that I will not be hurt Whether or not you get hurt in a bike accident is just a matter of luck If someone has a bad head injury they will be back at school or work in a few days Enabling I see bicycle helmets or advertisements for bicycle helmets in the stores Helmets cost too much Reinforcing Mom is the one person who most often says you should wear a helmet Do you personally know someone who has had a bad head injury?
Time 1: helmet use before intervention (n=384)
Time 2: helmet use immediately after intervention (n=363)
Time 3: helmet use 1 month after intervention (n=351)
−0.101* 0.072 0.034
−0.483*** 0.483*** 0.040
−0.388*** 0.329*** 0.077
−0.131** −0.004 −0.257†
−0.123** −0.024 −0.838†
−0.053 0 .016 −0.679***
0.030 −0.051 0.034 0.036 −0.035
−0.044 0.020 0.076 −0.039 −0.099
0.000 0.105* 0.094 0.023 0.002
0.105* −0.053
0.138** 0.000
0.084 0.046
0.111* −0.085
0.226† 0.054
0.172*** −0.002
*p
